Effectiveness of Technological based Classroom Engagement

Gupta, Aditya; Aggarwal, Vivek; Yadav, Prashant Dev; Naved, Mohd; Dash, Snigdha; Chandwani, Tushar

doi:10.1109/iciem54221.2022.9853199

Cited by 13 publications

(1 citation statement)

References 28 publications

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“…5 used deep learning methods to examine Student's head movements and facial expressions to determine various degrees of engagement. 6 discovered that utilizing technological tools can help assess levels of active learning and student engagement. The study by 7 developed a real-time system for student group engagement monitoring by analyzing their facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Classroom engagement evaluation using 360-degree view of the camera with deep learning techniques

Naidu

Bebina

bhatia

et al. 2023

Pattern Recognition and Tracking XXXIV

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Measuring classroom engagement is an important but challenging task in education. In this paper, we present an automated method for the assessment of the degree of classroom engagement using computer vision techniques that integrate data from multiple sensors, including the front and back of the student's seating arrangement. The students' engagement is evaluated based on attributes such as facial expression, gesture, head position, and distractions visible from the frontal view of the students. Moreover, using the videos from the back of the classroom, the professor's teaching content as well as their alignment with student engagement, are calculated. We leverage deep learning methods to extract emotion and behavior features to aid in the evaluation of engagement. These AI methods will quantify the classroom engagement process.

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Section: Introductionmentioning

confidence: 99%